In practice, decoders (equalizers) for data that are transmitted over channels with unknown distortion require the use of a known training sequence for "learning" the channel before any decoding is attempted (see, e.g., S. U. H. Quereshi, "Adaptive Equalization," Proceedings of the IEEE, Vol. 73, pp. 1349-1387, September 1985). Such learning typically involves deriving parameters (e.g., filter coefficients) that characterize the channel.
Sato, in his paper "A Method of Self-Recovering Equalization for Multilevel Amplitude Modulation," IEEE Transactions on Communications, Vol. COM-29, pp. 679-683, June 1975, demonstrated the feasibility of learning the channel, and hence performing equalization, without the help of a training sequence. This work of Sato on so-called "blind equalization" was later refined and analyzed for various applications (see, e.g., D. N. Goddard, "Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems," IEEE Transactions on Communications, Vol. COM-28, pp. 1867-1875, November 1980; A. Benveniste and M. Goursat, "Blind Equalizers," IEEE Transactions on Communications, Vol. COM-32, pp. 871-883, August 1984; G. J. Foschini, "Equalizing Without Altering or Detecting Data," Bell System Technical Journal, October 1985, pp. 1885-1912; G. Picchi and G. Prati, "Blind Equalization and Carrier Recovery Using a Stop-and-Go Decision Directed Algorithm," IEEE Transactions on Communications, Vol. COM-35, pp. 877-887, September 1987; N. K. Jablon, "Joint Blind Equalization, Carrier Recovery, and Timing Recovery for 64-QAM and 128-QAM Signal Constellations," Proc. ICC89, pp. 1043-1049; and O. Macchi and A. Hachicha, "Self-Adaptive equalization based on a prediction principle," Proc. IEEE GLOBECOM'86, pp. 1641-1645; and V. Weerackody). Existing algorithms for blind equalization typically require several thousand symbols to be received before they achieve reasonable convergence of the equalizer coefficients.
So-called decision-directed equalization algorithms have found many applications in data communications to track slow variations in a channel after an initial learning phase. In this mode, it is assumed that the output of the decoder is correct with high probability. Error signals based on these output symbols are then used to update the coefficients of the equalizer. If little or no training has been used, as in blind equalizer operation, the correct equalizer coefficients are not known initially. This causes the decisions at the equalizer output to be subject to a very high error probability, thus making further equalizer updates based on decision-feedback unreliable. Such decision-directed techniques have accordingly not heretofore been found suitable for use in blind equalization.
Recently, several new blind equalization algorithms have been developed which attempt to incorporate decision-directed techniques by automatically switching from a blind updating mode to a decision-directed mode whenever the error rates are judged to be low (see, O. Macchi and A. Hachicha, "Self-Adaptive Equalization Based on a Prediction Principle," Proc. IEEE GLOBECOM'86, pp. 1641-1645).